17th Jan 24

Talk Ideas with David Perez Abreu

Speaker: David Perez Abreu

Date: 24th of January, 2024

Time: 4:00 pm

Place: Room G4.1

Presentation title: “Towards zero-touch management in the Cloud-to-IoT continuum”

Short bio:

David Perez Abreu received his Ph.D. degree in Information Science and Technologies from the University of Coimbra in 2021. He has published several conference and journal papers and has actively participated in different national and international research projects. He is part of the program committee of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE International Workshop Smart Living and Communications for the Next Generations Networks (SLICO - WoWMoM), and IEEE International Conference on Design of Reliable Communication Networks (DRCN). His research interests include Cloud-to-IoT continuum, softwarized networks, as well as zero-touch network and service management. He worked as a researcher in the Laboratory for Mobile and Wireless Networks at the Central University of Venezuela from 2006 until 2014. He is an invited assistant professor at the Department of Informatics Engineering at the University of Coimbra and a senior researcher at the Laboratory for Informatics and Systems at the Pedro Nunes Institute.


The distributed and heterogeneous nature of the Cloud-to-IoT continuum and other network infrastructures, such as 5G and 6G networks, have boosted the need for automation of network and service management operations. Early automation solutions rely on optimization models and heuristics that for current conditions are not enough. Different paradigms have recently appeared, such as Zero touch network & Service Management (ZSM), designed by the ETSI to automatically orchestrate and manage network resources while assuring QoS requested by users. ZSM seeks to provide a cost-effective solution to both users and operators of communication networks by incorporating intelligence and autonomous adaptivity into network management. The goals are achieving three main characteristics: (1) self-organization; (2) self-optimization; and (3) self-healing. Cognitive approaches can be used by management modules that gather knowledge based on current and past observations; thus, Machine Learning (ML) algorithms can allow the collection, exchange, and usage of data to enhance the learning process, leading to a faster convergence, incurring in lower costs. This talk will present some perspectives based on early and more recent approaches to achieve automation for network and service management for the Cloud-to-IoT continuum.

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